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| تصميم الانحدار المتقطع الضبابي الديناميكي× | تصميم الانحدار الضبابي المتقطع× | |
|---|---|---|
| المجال | الاستدلال السببي | الاستدلال السببي |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 2001-2010 | 2001 |
| صاحب الطريقة≠ | Cellini, Ferreira & Rothstein (dynamic RDD, 2010); Hahn, Todd & Van der Klaauw (fuzzy RDD foundations, 2001) | Hahn, Todd & van der Klaauw |
| النوع | Quasi-experimental causal inference | Quasi-experimental causal inference |
| المصدر التأسيسي≠ | Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ | Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209. DOI ↗ |
| الأسماء البديلة | Dynamic Fuzzy RDD, DFRD, Time-varying Fuzzy RD, Dynamic Fuzzy RD Design | Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD |
| ذات صلة≠ | 4 | 5 |
| الملخص≠ | Dynamic Fuzzy Regression Discontinuity Design extends the standard fuzzy RDD to a panel or multi-period setting, allowing researchers to estimate how the causal effect of a probabilistic threshold-based treatment evolves over time. By combining an IV-based fuzzy first stage with time-indexed outcomes, it traces treatment effects across multiple post-treatment periods, not just at a single cross-sectional snapshot. | Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold. |
| ScholarGateمجموعة البيانات ↗ |
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